Applying Innovation Diffusion Theory to Determine Motivating Attributes for Successful Implementation of Internet-Based Interventions for Evidence Based Medicine: a Developing Country Context

they Abstract Current healthcare outcomes depend on the adoption of valid and latest research evidence and practicing evidence-based medicine (EBM). EBM is the process of adaptation of the finest available scientific research evidence into routine clinical practice. However, literature reports gap between actual and required clinical practice. This gap will not be bridged by just updating physicians about EBM. Therefore, it is required to study the motivational factors in a context to use technology in routine clinical practice as things behave differently under varying constraints. This study aimed to address this gap by investigating motivating factors to promote EBM, focusing mainly on developing country context. Innovation diffusion theory will be used to provide the basic or theoretical support for the research as this theory states that the adoption of any innovation is itself facilitated by its certain characteristics. Cross-sectional quantitative methodology will be used for this research. SPSS and SEM will be used to analyze data and validation of the tested research model. The innovation diffusion theory may provide constructive and practical insights into the factors for the successful implementation of EBM, as well as it will provide a guideline for those who try to adopt the best-evidences into their clinical practice.


Introduction
The innovation is a new idea that is observed by the individual.
Evidence-based medicine is an innovation in the clinical decisionmaking process, which promises to improve health care delivery [1]. But at the same time, practicing EBM is a paradigm shift that is changing the conventional way of the clinical decision-making process [2]. Acceptance of any change or innovation in conventional clinical practices is always very hard, and multiple factors play their part in the adoption procedure [3]. The characteristics of innovation play a significant role in defining its rate of adoption [4].
For an innovation to be adopted, it must be perceived as offering relative advantage, i.e., simple, compatible, observable and testable.
Roger proposes one of the theoretical approaches addressing the diffusion of innovation (DOI). DOI model is supportive at describing the acceptance of explicit clinical events, mostly when determining which components will need additional effort if diffusion is to ensure [4]. Literature showed applications of DOI in health departments as well [5][6][7]. Becker and Mohr worked to identify organizational characteristics linked with the diffusion process among the health department [6]. One study found that demographic features age, gender, education level, urban and rural areas had a great impact on the time for adoption of innovation. It was noted that the initial adopters of innovations different by age, education and used information-seeking approaches as compared to their jurisdictions who are varied by rurality [7,8]. Graduated younger people who had a higher standing in their graduating class and belonged to urban areas were ready to adopt less risky interventions. In comparison, older people in countryside areas who had a normal standing in their graduating class, and they 434 established their leadership roles were ready to take more risks, but they adopt less conventional innovations. It is also evident in literature that large health departments easily adopt innovations than small health departments. The accessibility of funds and human resources are the reasons which are supporting this finding [8,9].
The effective implementation of EBM in the healthcare sector, a better understanding of the motivating factors is required in detail. Relation and association of factors with each other are also very important as the context is very special, and physicians have autonomous authority in the clinical decision-making process. worldwide, yet the literature illustrates that it is not always an easy theory to relate empirically [10]. IDT identifies five factors that influence the diffusion and adoption of an innovative idea or strategy. The IDT offers a theoretical framework globally to accept information technology. The role of ethnical background plays an important part in the adoption of new ideas/technology as it addresses that how, why, and what adoption rate of new technology can relate with different social backgrounds and settings [11]. IDT not only addresses the adoption of information technology only, but it also addresses other diffusion processes through the society, such as the acceptance of new technology products such as services, style of music, fashion, food, ideas, or political candidates [11][12][13].
This study builds upon what is known from past research on the diffusion and research conducted by Jenine K. Harris [8], which described the significance to comprehend how health personnel perceives the relative advantage, simplicity, compatibility, and These are relative advantage, compatibility, complexity, trialability, and observability [14]. Definitions of all these four elements are given in Table 1 along with the hypothesized relations. All these variables are discussed below.

Constructs
Code Definitions Hypothesized Relation Relative advantage RA "The degree to which an innovation is perceived as better than using its precursors." [4] RA UI Compatibility CM "The degree to which an innovation is perceived as being consistent with the existing values, past experiences, and needs of potential adopters." [4] CM UI Complexity/ Simplicity SP "The degree to which an innovation is perceived as difficult to understand and use." [4] SP UI Trialability TR "The degree to which an innovation may be experimented with on a limited basis." [4] TR UI Observability to those people within the social system OB "The degree to which the results of an innovation are visible to others." [4] OB UI Usage Intention UI The subjective probability that he or she will engage in a given behavior [14] Dependent Variable

Independent variables
Relative advantage: The social prestige, satisfaction and convenience, clinicians are some important factors for measuring the degree of relative advantage. Relative advantage can also be measured in economic terms for a new clinical activity [3].

Sch J Psychol & Behav Sci
435 the clinicians without disturbing the balance of power distribution, then innovation will be readily accepted and adopted. Therefore, it is hypothesized that H1: Relative advantage of EBM has a noteworthy positive effect on the usage intention for EBM adoption.
Compatibility: It is necessary for successful adoption of an innovation that it must tackle an issue that is perceived as problematic by the clinicians. For instance, a new clinical activity or procedure will be adopted fast if it helps clinicians to detect cancer or other life-threatening illness at very early stages [15]. It is a strong medical belief that early detection of a disease is beneficial for the patients. Accordingly, clinical activity or procedure offering this capacity will be adopted quickly. The rapid adoption of mammography screening [16,17] and testing for prostate cancer are a few real-life examples. Though literature also has some controversial debates about the therapy mentioned above effectiveness.
Therefore, it is hypothesized that H2: Compatibility of EBM has a significant positive effect on the usage intention for EBM adoption.
Complexity/Simplicity: Literature proves that the probability of adoption for a clinical procedure increases when the procedure is simple, easy, and well defined. For example, the rate of change in drug regimen for patients by clinicians is high and the reason behind this phenomenon is that it is easy to adopt. While some precautionary activities as detecting and handling patients with harmful alcohol consumption [18] have not been adopted quickly, though reported potential health gain in literature. This may be due to the complexity of these activities. All preventions at the primary level are vulnerable due to the patient's resistance and their lack of accuracy in self-reporting risk behaviors. Additionally, inadequate expertise in the consulting skills of clinicians necessary to achieve change may be the other reason.
Therefore, it is hypothesized that H3: Complexity/simplicity of EBM has a significant positive effect usage intention for EBM adoption.
Trialability: According to Rogers, "trialability" is the degree of modification of an innovation. In other words, the capability to test an intervention in medicine on a limited basis allows clinicians to explore the implementation of the procedure, its acceptability, and the possible outcomes. Rogers claims that the ability to undertake limited cost-benefit experiments of an intervention endorses trust and confidence that the evidence is not only, but its implementation is logistically promising as well.
Therefore, it is hypothesized that

Data collection tool
A structured close-ended questionnaire was used. At the beginning of the questionnaire definition of evidence-based medicine was provided to have a better understanding of the concept. Demographic questions (were age, gender, organization, and working experience) were included at the beginning of the questionnaire. It was requested to the participants to give feedback by thinking that practice evidence-based medicine will be a requirement in the future for their routine clinical practice.
The standard variable for innovation diffusion theory (technical compatibility, simplicity, relative advantage, and intentions) was   Table 2 all values are within an acceptable range. After basic data screening process factor analysis is performed.

Factor analysis (FA)
FA is a multivariate regression analysis statistical method used to analyze the correlation structure between different variables/constructs. In FA, after identifying latent dimensions of the constructs, data reduction was performed. Exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) are two steps in FA. The data is explored in EFA, while hypothesis is tested in CFA [21]. For the current analysis, the researcher performed both steps of factor analysis. EFA: There are two main steps in EFA, which are extraction and rotation processes. The extraction process identifies the underlying factors or constructs, while the rotation process yields an easy presentation of a factor loading pattern. In the current analysis, the researcher used the orthogonal rotation method for factor loading. Before performing factor analysis, we must check the suitability of current research data for sample adequacy. For this purpose, we performed KMO and Bartlett's Test.

Kaiser-Meyer Olkin (KMO) and Bartlett's Test
For the current research study, the value of the KMO test is 0.853, which shows the confidence about sample size adequacy to proceed for further steps of factor analysis. The result for Bartlett's test can be interpreted on the value of significance. The value of significance is .000 confirmed that the analysis could continue by using factor analysis as tabulated in Table 3 Table 6. It is also found that the inter-construct correlation value was not above the square-root of the AVE, so the model satisfies the discriminant validity criterion. In Table 7, the fit indexes for both measurement and structural model are given. Hypothesis testing is done by using the structural model.  between observability and usage intention was that the software and information system had less observability by physicians, hence less rate of adoption as compare to hardware innovation [22].

Confirmatory factor analysis (CFA)
Consequently, the more potential user can see the innovation, the more likely he will adopt it.

Limitation
This study has following limitations: a) Cross-sectional self-reported data limit this study. b) Only one healthcare system, which might not reflect the factors for the successful diffusion of EBM in other health care settings.

c)
Using an anonymous survey for data collection.
d) The small sample sizes.

Limitations of SEM include
a) The use of a model development process to improve goodness of fit, which is sometimes referred to as a "post hoc" procedure for hypothesis formulation.
b) The use of goodness-of-fit measures to accept or reject a proposed model. These measures can inform the researcher whether a model is acceptable but cannot tell whether it is a superior model. Since in SEM the complete set is required for analysis, therefore imputing missing data could affect the analysis.

Conclusion
Evidence-based medicine holds promise in improving health